Efficient sparse estimation on interval-censored data with approximated L0 norm: Application to child mortality.
A novel penalty for the proportional hazards model under the interval-censored failure time data structure is discussed, with which the subject of variable selection is rarely studied. The penalty comes from an idea to approximate some information criterion, e.g., the BIC or AIC, and the core proces...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0249359 |
| _version_ | 1818364258593800192 |
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| author | Yan Chen Yulu Zhao |
| author_facet | Yan Chen Yulu Zhao |
| author_sort | Yan Chen |
| collection | DOAJ |
| description | A novel penalty for the proportional hazards model under the interval-censored failure time data structure is discussed, with which the subject of variable selection is rarely studied. The penalty comes from an idea to approximate some information criterion, e.g., the BIC or AIC, and the core process is to smooth the ℓ0 norm. Compared with usual regularization methods, the proposed approach is free of heavily time-consuming hyperparameter tuning. The efficiency is further improved by fitting the model and selecting variables in one step. To achieve this, sieve likelihood is introduced, which simultaneously estimates the coefficients and baseline cumulative hazards function. Furthermore, it is shown that the three desired properties for penalties, i.e., continuity, sparsity, and unbiasedness, are all guaranteed. Numerical results show that the proposed sparse estimation method is of great accuracy and efficiency. Finally, the method is used on data of Nigerian children and the key factors that have effects on child mortality are found. |
| first_indexed | 2024-12-13T22:01:31Z |
| format | Article |
| id | doaj.art-064bc21a67c94340842edec85f5d0fd5 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T22:01:31Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-064bc21a67c94340842edec85f5d0fd52022-12-21T23:30:00ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01164e024935910.1371/journal.pone.0249359Efficient sparse estimation on interval-censored data with approximated L0 norm: Application to child mortality.Yan ChenYulu ZhaoA novel penalty for the proportional hazards model under the interval-censored failure time data structure is discussed, with which the subject of variable selection is rarely studied. The penalty comes from an idea to approximate some information criterion, e.g., the BIC or AIC, and the core process is to smooth the ℓ0 norm. Compared with usual regularization methods, the proposed approach is free of heavily time-consuming hyperparameter tuning. The efficiency is further improved by fitting the model and selecting variables in one step. To achieve this, sieve likelihood is introduced, which simultaneously estimates the coefficients and baseline cumulative hazards function. Furthermore, it is shown that the three desired properties for penalties, i.e., continuity, sparsity, and unbiasedness, are all guaranteed. Numerical results show that the proposed sparse estimation method is of great accuracy and efficiency. Finally, the method is used on data of Nigerian children and the key factors that have effects on child mortality are found.https://doi.org/10.1371/journal.pone.0249359 |
| spellingShingle | Yan Chen Yulu Zhao Efficient sparse estimation on interval-censored data with approximated L0 norm: Application to child mortality. PLoS ONE |
| title | Efficient sparse estimation on interval-censored data with approximated L0 norm: Application to child mortality. |
| title_full | Efficient sparse estimation on interval-censored data with approximated L0 norm: Application to child mortality. |
| title_fullStr | Efficient sparse estimation on interval-censored data with approximated L0 norm: Application to child mortality. |
| title_full_unstemmed | Efficient sparse estimation on interval-censored data with approximated L0 norm: Application to child mortality. |
| title_short | Efficient sparse estimation on interval-censored data with approximated L0 norm: Application to child mortality. |
| title_sort | efficient sparse estimation on interval censored data with approximated l0 norm application to child mortality |
| url | https://doi.org/10.1371/journal.pone.0249359 |
| work_keys_str_mv | AT yanchen efficientsparseestimationonintervalcensoreddatawithapproximatedl0normapplicationtochildmortality AT yuluzhao efficientsparseestimationonintervalcensoreddatawithapproximatedl0normapplicationtochildmortality |